BEV Lane Detection.
In order to obtain more accurate road cognition results, much works have turned their attention to lane detection in 3D space. [1]}, [2]}, [3]}, [4]} use sota results to prove the feasibility of using CNN network for 3D lane detection in monocular images. [1]} introduces a unified network for encoding 2D image information, spatial transform and 3D lane detecton in two path-ways: the Image-View pathway encodes features from 2D image, while the Top-view pathway provides translation-invariant features for 3D lane Detection. [2]} proposes an extensible two-stage framework, which separates the image segmentation sub-network and the geometry encoding sub-network. The specific method is to train the 2D lane segmentation part separately, and combine the camera intrinsic and extrinsic parameters to convert the 2D segmentation mask to IPM project into virtual top-view, and then perform anchor-based 3D lane detection in the virtual top-view. [3]} proposes a unified 2D lane detection and 3D lane detection framework, and introduces trasformer into the spatial transformation module to obtain more robust feature expression, which is the current sota in the direction of 3D lane detection, and proposes a real scene based Large-scale annotated 3D lane dataset, OpenLane.
